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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">注解</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-how-to-compile-models-from-paddle-py"><span class="std std-ref">here</span></a> to download the full example code</p>
</div>
<div class="sphx-glr-example-title section" id="compile-paddlepaddle-models">
<span id="sphx-glr-how-to-compile-models-from-paddle-py"></span><h1>Compile PaddlePaddle Models<a class="headerlink" href="#compile-paddlepaddle-models" title="永久链接至标题">¶</a></h1>
<p><strong>Author</strong>: <a class="reference external" href="https://github.com/ZiyuanMa/">Ziyuan Ma</a></p>
<p>This article is an introductory tutorial to deploy PaddlePaddle models with Relay.
For us to begin with, PaddlePaddle&gt;=2.1.3 is required to be installed.
A quick solution is</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
</pre></div>
</div>
<p>or please refer to official site.
<a class="reference external" href="https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html">https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/linux-pip.html</a></p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tarfile</span>
<span class="kn">import</span> <span class="nn">paddle</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">tvm</span>
<span class="kn">from</span> <span class="nn">tvm</span> <span class="k">import</span> <span class="n">relay</span>
<span class="kn">from</span> <span class="nn">tvm.contrib.download</span> <span class="k">import</span> <span class="n">download_testdata</span>
</pre></div>
</div>
<div class="section" id="load-pretrained-resnet50-model">
<h2>Load pretrained ResNet50 model<a class="headerlink" href="#load-pretrained-resnet50-model" title="永久链接至标题">¶</a></h2>
<p>We load a pretrained ResNet50 provided by PaddlePaddle.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">url</span> <span class="o">=</span> <span class="s2">&quot;https://bj.bcebos.com/x2paddle/models/paddle_resnet50.tar&quot;</span>
<span class="n">model_path</span> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="s2">&quot;paddle_resnet50.tar&quot;</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">&quot;model&quot;</span><span class="p">)</span>

<span class="k">with</span> <span class="n">tarfile</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">model_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">tar</span><span class="p">:</span>
    <span class="n">names</span> <span class="o">=</span> <span class="n">tar</span><span class="o">.</span><span class="n">getnames</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">names</span><span class="p">:</span>
        <span class="n">tar</span><span class="o">.</span><span class="n">extract</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="s2">&quot;./&quot;</span><span class="p">)</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">paddle</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;./paddle_resnet50/model&quot;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="load-a-test-image">
<h2>加载一张测试图片<a class="headerlink" href="#load-a-test-image" title="永久链接至标题">¶</a></h2>
<p>样例的主角是只猫！</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">paddle.vision.transforms</span> <span class="k">as</span> <span class="nn">T</span>


<span class="n">transforms</span> <span class="o">=</span> <span class="n">T</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
    <span class="p">[</span>
        <span class="n">T</span><span class="o">.</span><span class="n">Resize</span><span class="p">((</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">)),</span>
        <span class="n">T</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
        <span class="n">T</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
        <span class="n">T</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">],</span> <span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]),</span>
    <span class="p">]</span>
<span class="p">)</span>

<span class="n">img_url</span> <span class="o">=</span> <span class="s2">&quot;https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true&quot;</span>
<span class="n">img_path</span> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><span class="n">img_url</span><span class="p">,</span> <span class="s2">&quot;cat.png&quot;</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="o">.</span><span class="n">resize</span><span class="p">((</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span>

<span class="n">img</span> <span class="o">=</span> <span class="n">transforms</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="compile-the-model-with-relay">
<h2>通过 Realy 编译模型<a class="headerlink" href="#compile-the-model-with-relay" title="永久链接至标题">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">target</span> <span class="o">=</span> <span class="s2">&quot;llvm&quot;</span>
<span class="n">shape_dict</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">}</span>
<span class="n">mod</span><span class="p">,</span> <span class="n">params</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">frontend</span><span class="o">.</span><span class="n">from_paddle</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">shape_dict</span><span class="p">)</span>

<span class="k">with</span> <span class="n">tvm</span><span class="o">.</span><span class="n">transform</span><span class="o">.</span><span class="n">PassContext</span><span class="p">(</span><span class="n">opt_level</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
    <span class="n">executor</span> <span class="o">=</span> <span class="n">relay</span><span class="o">.</span><span class="n">build_module</span><span class="o">.</span><span class="n">create_executor</span><span class="p">(</span>
        <span class="s2">&quot;graph&quot;</span><span class="p">,</span> <span class="n">mod</span><span class="p">,</span> <span class="n">tvm</span><span class="o">.</span><span class="n">cpu</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">target</span><span class="p">,</span> <span class="n">params</span>
    <span class="p">)</span><span class="o">.</span><span class="n">evaluate</span><span class="p">()</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>One or more operators have not been tuned. Please tune your model for better performance. Use DEBUG logging level to see more details.
</pre></div>
</div>
</div>
<div class="section" id="execute-on-tvm">
<h2>在 TVM 上执行<a class="headerlink" href="#execute-on-tvm" title="永久链接至标题">¶</a></h2>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">dtype</span> <span class="o">=</span> <span class="s2">&quot;float32&quot;</span>
<span class="n">tvm_output</span> <span class="o">=</span> <span class="n">executor</span><span class="p">(</span><span class="n">tvm</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)))</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="section" id="look-up-synset-name">
<h2>在同义词集合（synset）中查找类别名称<a class="headerlink" href="#look-up-synset-name" title="永久链接至标题">¶</a></h2>
<p>在有1000个类的同义词集合（synset）中查找预测排名第一的类别索引。</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">synset_url</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
    <span class="p">[</span>
        <span class="s2">&quot;https://gist.githubusercontent.com/zhreshold/&quot;</span><span class="p">,</span>
        <span class="s2">&quot;4d0b62f3d01426887599d4f7ede23ee5/raw/&quot;</span><span class="p">,</span>
        <span class="s2">&quot;596b27d23537e5a1b5751d2b0481ef172f58b539/&quot;</span><span class="p">,</span>
        <span class="s2">&quot;imagenet1000_clsid_to_human.txt&quot;</span><span class="p">,</span>
    <span class="p">]</span>
<span class="p">)</span>
<span class="n">synset_name</span> <span class="o">=</span> <span class="s2">&quot;imagenet1000_clsid_to_human.txt&quot;</span>
<span class="n">synset_path</span> <span class="o">=</span> <span class="n">download_testdata</span><span class="p">(</span><span class="n">synset_url</span><span class="p">,</span> <span class="n">synset_name</span><span class="p">,</span> <span class="n">module</span><span class="o">=</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">synset_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">synset</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>

<span class="n">top1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">tvm_output</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;TVM prediction top-1 id: </span><span class="si">{top1}</span><span class="s2">, class name: </span><span class="si">{synset[top1]}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
</div>
<p class="sphx-glr-script-out">Out:</p>
<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>TVM prediction top-1 id: 282, class name:  282: &#39;tiger cat&#39;,
</pre></div>
</div>
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